code: def test_magnitude(self, device, dtype):
      inp = torch.tensor(
          [
              [
                  [
                      [0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 1.0, 0.0, 0.0],
                      [0.0, 1.0, 1.0, 1.0, 0.0],
                      [0.0, 0.0, 1.0, 0.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      expected_magnitude = torch.tensor(
          [
              [
                  [
                      [0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 1.2458, 0.9672, 1.2458, 0.0],
                      [0.0, 0.9672, 0.0, 0.9672, 0.0],
                      [0.0, 1.2458, 0.9672, 1.2458, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      expected_edges = torch.tensor(
          [
              [
                  [
                      [0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 1.0, 1.0, 1.0, 0.0],
                      [0.0, 1.0, 0.0, 1.0, 0.0],
                      [0.0, 1.0, 1.0, 1.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      magnitude, edges = canny(inp)
  
      self.assert_close(
          magnitude, expected_magnitude, atol=1e-4, rtol=1e-4
      )
      self.assert_close(edges, expected_edges, atol=1e-4, rtol=1e-4)
  
file_path: kornia/test/filters/test_canny.py
imports: import pytest
  import torch
  from kornia.filters import Canny, canny
  from kornia.testing import BaseTester, tensor_to_gradcheck_var
  from kornia.utils._compat import torch_version
label_desc: Use image node_id 5 for calling the TestCanny obj's underlying member method code with example usage: obj.test_magnitude(device, dtype) without return types